Tag Archives: Eko Prasetyo

ICTs and data are increasingly being used for monitoring, evaluation, research and learning (MERL). MERL Tech London was an open space for practitioners, techies, researchers and decision makers to discuss their good and not so good experiences. This blogpost is a reflection of the debates that took place during the conference.

Is data literacy still a thing?

Data literacy is “the ability to consume for knowledge, produce coherently and think critically about data.” The perception of data literacy varies depending on the stakeholder’s needs. Being data literate for an M&E team, for example, means possessing statistics skills including collecting and combining large data sets. Program team requires different level of data literacy: the competence to carefully interpret and communicate meaningful stories using processed data (or information) to reach the target audiences.

Data literacy is – and will remain – a priority in development. The current debate is no longer about whether an organisation should use data or not. It’s rather how well the organisation can use data to achieve their objectives. Yet, organisation’s efforts are often concentrated in just one part of the information value chain, data collection. Data collection in itself is not the end goal. Data has to be processed into information and knowledge for making informed decisions and actions.

This doesn’t necessary imply that the decision making is purely based on data, nor that data can replace the role of decision makers. Quite the opposite: data-informed decision making strikes balance between expertise and information. It also takes data limitations into account. Nevertheless, one can’t become a data-informed organisation without being data literate.

What’s your organisation’s data strategy?

The journey of becoming a data-informed organisation can take some time. Poor data quality, duplication efforts and underinvestment are classic obstacles requiring a systematic solution (see Tweet). The commitment from senior management team should be secured for that. Data team has to be established. Staff members need access to relevant data platforms and training. More importantly, the organisation has to embrace the cultural change towards valuing evidence and acting on positive and negative findings

Organisations seek to balance between (data) demands and priorities. Some invest hundreds of thousands dollars for setting up a data team to articulate the organisation’s needs and priorities, as well as to mobilise technical support. A 3-5 years strategic plan is created to coordinate efforts between country offices.

Others take a more modest approach. They recruit few data scientists to support MERL activities of analysing particularly large amounts of project data. The data scientist role evolves along the project growth. In both cases, leadership is the key driver for shifting the culture towards becoming a data-informed organisation.

Should an organisation use certain data because it can?

The organisation working with data usually faces challenges around privacy, legality, ethics and grey areas, such as bias and power dynamics between data collectors and their target groups. The use of biometric data in humanitarian settings is an example where all these tensions collide. Biometric data, e.g. fingerprint, iris scan, facial recognition – is powerful, yet invasive. While proven beneficial, biometric data is vulnerable to data breach and misuse, e.g. profiling and tracking. The practice raises critical questions: does the target group, e.g. refugees, have the option to refuse handling over their sensitive personal data? If so, will they still be entitled to receive aid assistance? To what extent the target group is aware how their sensitive personal data will be used and shared, including in the unforeseen circumstances?

The people’s privacy, safety and security are main priorities in any data work. The organisation should uphold the highest standards and set an example. In those countries where regulatory frameworks are lagging behind data and technology, organisations shouldn’t abuse their power. When the risk of using a certain data outweighs the benefits, or in doubt, the organisation should take a pause and ask itself some necessary questions from the perspective of its target groups. Oxfam which dismissed – following two years of internal discussions and intensive research – the idea of using biometric data in any of their project should be seen as a positive example.

To conclude, the benefits of data can only be realised when an organisation enjoys visionary leadership, sufficient capacity and upholds its principles. No doubts, this is easier being said than done; it requires time and patience. All these efforts, however, are necessary for a high-achieving organisations.